CluedIn and Telefónica Tech announce new partnership
CluedIn and Telefónica Tech announce new partnership to accelerate data-driven value for Microsoft customers
Modern MDM: Moving Beyond Traditional Reference Data
Introduction
In the world of traditional data management, reference data and master data are treated as two different categories of data. Reference data is used to classify or categorize other data, and master data is business-critical data shared by multiple systems, applications, and processes. Conventionally, examples of master data include customer data, product records, and vendor data. Reference data includes code lists, taxonomies, and hierarchies of data, amongst other things.
Five Scenarios Where Graph-Based MDM Shines Over Relational Databases
Introduction
Believe it or not, the structure of your MDM system can dramatically affect your organization's ability to derive insights and value from data. Traditional Master Data Management (MDM) systems are built using relational databases, whereas more modern platforms utilize Graph. With their superior handling of interconnected data, graph-based systems offer distinct advantages in several key scenarios over traditional relational databases.
CluedIn announces third-party Purview experience
The new Microsoft Purview Data Governance experience
The new Microsoft Purview Data Governance experience, announced at today’s Microsoft Fabric Community Conference, enables every organization to accelerate the creation of responsible value from its data.
By simplifying operations through AI-powered assistance and promoting federated governance across multi-cloud data estates, Microsoft Purview now offers a sophisticated yet straightforward business-friendly interface, enhancing integration across data sources and paving the way for AI-driven natural language experiences.
This new SaaS offering, currently available in preview, is built upon Microsoft's own data governance journey and lessons learned from its enterprise customers, embodying principles of unified, integrated, and extensible data management solutions. With this advancement, Microsoft aims to empower organizations to govern, protect, and manage data effectively, ensuring readiness for AI applications while addressing the challenges of cybersecurity, regulatory compliance, and the need for actionable data insights.
Augmented Data Quality: the what, why, when and how
What is Augmented Data Quality?
Augmented Data Quality (ADQ) leverages advanced technologies such as AI and ML to enhance traditional data management practices. It automates the detection and correction of data issues, enabling data leaders, data stewards, and domain experts to ensure higher data accuracy, consistency, and reliability. This approach is vital in today's fast-paced data environments, where manual oversight is impractical due to the sheer volume and complexity of data. ADQ represents a shift towards more dynamic, responsive data governance models, aligning closely with strategic business objectives by providing a cleaner, more trustworthy data foundation.
When is Augmented Data Quality needed?
ADQ becomes crucial in various scenarios, such as when organizations face rapid data growth, deal with data from multiple, disparate sources, or require real-time data analysis for decision-making. It's also essential when businesses undertake digital transformation projects that necessitate clean, reliable data for new applications. In addition, industries regulated by strict data compliance standards need augmented solutions to ensure accuracy and adherence to regulations. Essentially, any situation where the volume, velocity, or variety of data overwhelms traditional management approaches calls for augmented data quality solutions.
5 Reasons Why CluedIn is the Optimal Choice for MDM on Microsoft Azure
Enterprises constantly seek solutions that not only streamline their data processes but also align seamlessly with their existing technology stack. For businesses that have committed to the Microsoft Azure ecosystem, CluedIn is the go-to Master Data Management (MDM) solution. Here we explore five fundamental reasons why CluedIn stands as the most natural choice for Master Data Management on Microsoft Azure.
CluedIn unveils next-generation Master Data Management powered by AI
New CluedIn AI Assistant helps organizations to integrate data 60% faster, validate and deduplicate data 50% quicker, and improve overall data quality by 30%
CluedIn today announced that its new release is now available in private preview with an anticipated GA date of early 2024. The latest version represents another significant leap forward in reinforcing CluedIn’s position as the most advanced MDM system available, in terms of both enabling domain experts and accelerating the process of extracting value from enterprise data.
Your personal MDM assistant
In an industry first, CluedIn now includes an AI Assistant feature to support data stewards, engineers, and domain experts with the day-to-day management and operationalization of data. The CluedIn AI Assistant will help with the generation of rules, mapping, and providing additional context to specific pieces of data. CluedIn customers can also expect AI-aided generation of deduplication rules and project recommendations in the near future. Learn more about how the CluedIn AI Assistant could help you save days of manual effort and aid in bridging the data literacy gap here.
The new release builds on CluedIn’s commitment to pioneering the use of Generative AI in MDM, as demonstrated by the announcement that CluedIn was the first MDM vendor to integrate with Azure OpenAI in April 2023.
How to improve data quality without a Master Data Management platform
The quality of your data can make or break your business. While Master Data Management (MDM) solutions are often touted as the go-to for ensuring high-quality data, it is possible to make improvements without them.
Here are six ways to increase the quality of your data without using an MDM solution: